Log softmax nonstd input shape (#740)

* fix a bug that softmax/logsoftmax cannot handle nonstd input shape

* clang format

* fix review comments

* clang format

* refine test to have more code coverage

* clang format
Co-authored-by: mvermeulen <5479696+mvermeulen@users.noreply.github.com>

src/include/migraphx/op/logsoftmax.hpp

@@ -30,9 +30,16 @@ struct logsoftmax
     std::string name() const { return "logsoftmax"; }
     shape normalize_compute_shape(std::vector<shape> inputs) const
     {
-        check_shapes{inputs, *this}.has(1).standard();
+        if(inputs.at(0).packed())
+        {
             return inputs.at(0);
         }
+        else
+        {
+            auto lens = inputs.at(0).lens();
+            return {inputs.at(0).type(), lens};
+        }
+    }
 
     auto output() const
     {

src/include/migraphx/op/softmax.hpp

@@ -30,9 +30,17 @@ struct softmax
     std::string name() const { return "softmax"; }
     shape normalize_compute_shape(std::vector<shape> inputs) const
     {
-        check_shapes{inputs, *this}.has(1).standard();
+        check_shapes{inputs, *this}.has(1);
+        if(inputs.at(0).packed())
+        {
             return inputs.at(0);
         }
+        else
+        {
+            auto lens = inputs.at(0).lens();
+            return {inputs.at(0).type(), lens};
+        }
+    }
 
     auto output() const
     {

src/targets/cpu/lowering.cpp

@@ -403,6 +403,7 @@ struct cpu_softmax : auto_register_op<cpu_softmax<Op>>
     std::string name() const { return "cpu::" + op.name(); }
     shape compute_shape(const std::vector<shape>& inputs) const
     {
+        check_shapes{inputs, *this}.has(1).standard();
         return op.normalize_compute_shape(inputs);
     }
     argument compute(context&, const shape& output_shape, std::vector<argument> args) const

test/onnx/gen_onnx.py

@@ -1821,6 +1821,24 @@ def logsoftmax_test():
     return ([node], [x], [y])
 
 
+@onnx_test
+def logsoftmax_nonstd_input_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [6, 9])
+    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 4])
+    z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3, 4])
+
+    node0 = onnx.helper.make_node('Slice',
+                                  inputs=['0'],
+                                  axes=[0, 1],
+                                  starts=[1, 0],
+                                  ends=[4, 4],
+                                  outputs=['1'])
+
+    node1 = onnx.helper.make_node('LogSoftmax', inputs=['1'], outputs=['2'])
+
+    return ([node0, node1], [x], [z])
+
+
 @onnx_test
 def lrn_test():
     x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 28, 24, 24])
@@ -2951,6 +2969,24 @@ def softmax_test():
     return ([node], [x], [y])
 
 
+@onnx_test
+def softmax_nonstd_input_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [6, 8])
+    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 4])
+    z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3, 4])
+
+    node0 = onnx.helper.make_node('Slice',
+                                  inputs=['0'],
+                                  axes=[0, 1],
+                                  starts=[1, 0],
+                                  ends=[4, 4],
+                                  outputs=['1'])
+
+    node1 = onnx.helper.make_node('Softmax', inputs=['1'], outputs=['2'])
+
+    return ([node0, node1], [x], [z])
+
+
 @onnx_test
 def split_minus_axis_test():
     x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10, 15])

test/onnx/onnx_test.cpp

@@ -1657,6 +1657,21 @@ TEST_CASE(logsoftmax_test)
     EXPECT(p == prog);
 }
 
+TEST_CASE(logsoftmax_nonstd_input_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    auto l0  = mm->add_parameter("0", migraphx::shape{migraphx::shape::float_type, {6, 9}});
+    auto l1  = mm->add_instruction(
+        migraphx::make_op("slice", {{"axes", {0, 1}}, {"starts", {1, 0}}, {"ends", {4, 4}}}), l0);
+    auto l2 = mm->add_instruction(migraphx::make_op("logsoftmax", {{"axis", 1}}), l1);
+    mm->add_return({l2});
+
+    auto prog = migraphx::parse_onnx("logsoftmax_nonstd_input_test.onnx");
+
+    EXPECT(p == prog);
+}
+
 TEST_CASE(lrn_test)
 {
     migraphx::program p;
@@ -2715,6 +2730,21 @@ TEST_CASE(softmax_test)
     EXPECT(p == prog);
 }
 
+TEST_CASE(softmax_nonstd_input_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    auto l0  = mm->add_parameter("0", migraphx::shape{migraphx::shape::float_type, {6, 8}});
+    auto l1  = mm->add_instruction(
+        migraphx::make_op("slice", {{"axes", {0, 1}}, {"starts", {1, 0}}, {"ends", {4, 4}}}), l0);
+    auto l2 = mm->add_instruction(migraphx::make_op("softmax", {{"axis", 1}}), l1);
+    mm->add_return({l2});
+
+    auto prog = migraphx::parse_onnx("softmax_nonstd_input_test.onnx");
+
+    EXPECT(p == prog);
+}
+
 TEST_CASE(split_minus_axis_test)
 {
     migraphx::program p;

test/verify/test_logsoftmax1.cpp

+
+#include "verify_program.hpp"
+#include <migraphx/program.hpp>
+#include <migraphx/generate.hpp>
+#include <migraphx/make_op.hpp>
+
+struct test_logsoftmax1 : verify_program<test_logsoftmax1>
+{
+    migraphx::program create_program() const
+    {
+        migraphx::program p;
+        auto* mm = p.get_main_module();
+        auto x = mm->add_parameter("x", migraphx::shape{migraphx::shape::float_type, {5, 3, 3, 4}});
+        auto tx = mm->add_instruction(migraphx::make_op("transpose", {{"dims", {2, 3, 0, 1}}}), x);
+        auto r  = mm->add_instruction(migraphx::make_op("logsoftmax", {{"axis", 0}}), tx);
+        mm->add_return({r});
+        return p;
+    }
+};

test/verify/test_softmax3.cpp

+
+#include "verify_program.hpp"
+#include <migraphx/program.hpp>
+#include <migraphx/generate.hpp>
+#include <migraphx/make_op.hpp>
+
+struct test_softmax3 : verify_program<test_softmax3>
+{
+    migraphx::program create_program() const
+    {
+        migraphx::program p;
+        auto* mm = p.get_main_module();
+        auto x = mm->add_parameter("x", migraphx::shape{migraphx::shape::float_type, {5, 3, 3, 4}});
+        auto sx = mm->add_instruction(
+            migraphx::make_op("slice", {{"axes", {0, 3}}, {"starts", {1, 1}}, {"ends", {5, 4}}}),
+            x);
+        auto r = mm->add_instruction(migraphx::make_op("softmax", {{"axis", 0}}), sx);
+        mm->add_return({r});
+        return p;
+    }
+};